





'''simple docstring'''







import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')






def 					_lowerCamelCase							(					lowercase		: np.ndarray		)      ->      int:
				return input_array.reshape((input_array.size, 1)		)






def 					_lowerCamelCase							(					lowercase		: np.ndarray       ,				lowercase		: np.ndarray       ,				lowercase		: int		)      ->      int:
				_a				      =					np.nan
				for i in range(lowercase		):
								_a				      =					features[:, labels == i]
								_a				      =					data.mean(1		)
								# Centralize the data of class i
								_a				      =					data - column_reshape(lowercase		)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += np.dot(lowercase       ,				centered_data.T		)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_a				      =					np.dot(lowercase       ,				centered_data.T		)

				return covariance_sum / features.shape[1]






def 					_lowerCamelCase							(					lowercase		: np.ndarray       ,				lowercase		: np.ndarray       ,				lowercase		: int		)      ->      Tuple:
				_a				      =					features.mean(1		)
				_a				      =					np.nan
				for i in range(lowercase		):
								_a				      =					features[:, labels == i]
								_a				      =					data.shape[1]
								_a				      =					data.mean(1		)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += device_data * np.dot(
												    column_reshape(lowercase		) - column_reshape(lowercase		)       ,				(column_reshape(lowercase		) - column_reshape(lowercase		)).T       ,				)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_a				      =					device_data * np.dot(
												    column_reshape(lowercase		) - column_reshape(lowercase		)       ,				(column_reshape(lowercase		) - column_reshape(lowercase		)).T       ,				)

				return covariance_sum / features.shape[1]






def 					_lowerCamelCase							(					lowercase		: np.ndarray       ,				lowercase		: int		)      ->      str:




				if features.any():
								_a				      =					features.mean(1		)
								# Center the dataset
								_a				      =					features - np.reshape(lowercase       ,				(data_mean.size, 1)		)
								_a				      =					np.dot(lowercase       ,				centered_data.T		) / features.shape[1]
								_a      ,   _a				      =					np.linalg.eigh(lowercase		)
								# Take all the columns in the reverse order (-1), and then takes only the first
								_a				      =					eigenvectors[:, ::-1][:, 0:dimensions]
								# Project the database on the new space
								_a				      =					np.dot(filtered_eigenvectors.T       ,				lowercase		)
								logging.info("Principal Component Analysis computed"		)

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=lowercase		)
								logging.error("Dataset empty"		)
								raise AssertionError






def 					_lowerCamelCase							(					lowercase		: np.ndarray       ,				lowercase		: np.ndarray       ,				lowercase		: int       ,				lowercase		: int		)      ->      Any:
				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								_a      ,   _a				      =					eigh(
								    covariance_between_classes(lowercase       ,				lowercase       ,				lowercase		)       ,				covariance_within_classes(lowercase       ,				lowercase       ,				lowercase		)       ,				)
								_a				      =					eigenvectors[:, ::-1][:, :dimensions]
								_a      ,   _a      ,   _a				      =					np.linalg.svd(lowercase		)
								_a				      =					svd_matrix[:, 0:dimensions]
								_a				      =					np.dot(filtered_svd_matrix.T       ,				lowercase		)
								logging.info("Linear Discriminant Analysis computed"		)

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=lowercase		)
								logging.error("Dataset empty"		)
								raise AssertionError






def 					_lowerCamelCase							(					)      ->      str:
				_a				      =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
				_a				      =					np.array([0, 0, 0, 1, 1]		)
				_a				      =					2
				_a				      =					2

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(lowercase		) as error_info:
								_a				      =					linear_discriminant_analysis(
								    lowercase       ,				lowercase       ,				lowercase       ,				lowercase		)
								if isinstance(lowercase       ,				np.ndarray		):
												raise AssertionError(
												    "Did not raise AssertionError for dimensions > classes"		)
								assert error_info.type is AssertionError






def 					_lowerCamelCase							(					)      ->      int:
				_a				      =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
				_a				      =					2
				_a				      =					np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]		)

				with pytest.raises(lowercase		) as error_info:
								_a				      =					principal_component_analysis(lowercase       ,				lowercase		)
								if not np.allclose(lowercase       ,				lowercase		):
												raise AssertionError
								assert error_info.type is AssertionError


if __name__ == "__main__":
					import doctest

					doctest.testmod()







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






